Predictive Modeling of House Prices in King County

Introduction

This project aims to predict house prices using regression and machine learning techniques on the King County housing dataset.

Data Preparation

data <- read.csv("kc_house_data.csv")
data <- data %>% select(-id, -date)  # Remove non-informative columns
summary(data)

##      price            bedrooms        bathrooms      sqft_living   
##  Min.   :  75000   Min.   : 0.000   Min.   :0.000   Min.   :  290  
##  1st Qu.: 321950   1st Qu.: 3.000   1st Qu.:1.750   1st Qu.: 1427  
##  Median : 450000   Median : 3.000   Median :2.250   Median : 1910  
##  Mean   : 540088   Mean   : 3.371   Mean   :2.115   Mean   : 2080  
##  3rd Qu.: 645000   3rd Qu.: 4.000   3rd Qu.:2.500   3rd Qu.: 2550  
##  Max.   :7700000   Max.   :33.000   Max.   :8.000   Max.   :13540  
##     sqft_lot           floors        waterfront            view       
##  Min.   :    520   Min.   :1.000   Min.   :0.000000   Min.   :0.0000  
##  1st Qu.:   5040   1st Qu.:1.000   1st Qu.:0.000000   1st Qu.:0.0000  
##  Median :   7618   Median :1.500   Median :0.000000   Median :0.0000  
##  Mean   :  15107   Mean   :1.494   Mean   :0.007542   Mean   :0.2343  
##  3rd Qu.:  10688   3rd Qu.:2.000   3rd Qu.:0.000000   3rd Qu.:0.0000  
##  Max.   :1651359   Max.   :3.500   Max.   :1.000000   Max.   :4.0000  
##    condition         grade          sqft_above   sqft_basement   
##  Min.   :1.000   Min.   : 1.000   Min.   : 290   Min.   :   0.0  
##  1st Qu.:3.000   1st Qu.: 7.000   1st Qu.:1190   1st Qu.:   0.0  
##  Median :3.000   Median : 7.000   Median :1560   Median :   0.0  
##  Mean   :3.409   Mean   : 7.657   Mean   :1788   Mean   : 291.5  
##  3rd Qu.:4.000   3rd Qu.: 8.000   3rd Qu.:2210   3rd Qu.: 560.0  
##  Max.   :5.000   Max.   :13.000   Max.   :9410   Max.   :4820.0  
##     yr_built     yr_renovated       zipcode           lat       
##  Min.   :1900   Min.   :   0.0   Min.   :98001   Min.   :47.16  
##  1st Qu.:1951   1st Qu.:   0.0   1st Qu.:98033   1st Qu.:47.47  
##  Median :1975   Median :   0.0   Median :98065   Median :47.57  
##  Mean   :1971   Mean   :  84.4   Mean   :98078   Mean   :47.56  
##  3rd Qu.:1997   3rd Qu.:   0.0   3rd Qu.:98118   3rd Qu.:47.68  
##  Max.   :2015   Max.   :2015.0   Max.   :98199   Max.   :47.78  
##       long        sqft_living15    sqft_lot15    
##  Min.   :-122.5   Min.   : 399   Min.   :   651  
##  1st Qu.:-122.3   1st Qu.:1490   1st Qu.:  5100  
##  Median :-122.2   Median :1840   Median :  7620  
##  Mean   :-122.2   Mean   :1987   Mean   : 12768  
##  3rd Qu.:-122.1   3rd Qu.:2360   3rd Qu.: 10083  
##  Max.   :-121.3   Max.   :6210   Max.   :871200

Exploratory Data Analysis (EDA)

# Correlation matrix
corrplot(cor(data %>% select_if(is.numeric)), method = "color")

# Distribution of price
ggplot(data, aes(price)) + geom_histogram(bins = 50, fill = "steelblue", color = "white")

Data Splitting

set.seed(123)
split <- createDataPartition(data$price, p = 0.8, list = FALSE)
train <- data[split, ]
test <- data[-split, ]

Model Building and Evaluation

Linear Regression

lm_model <- lm(price ~ ., data = train)
pred_lm <- predict(lm_model, test)
rmse_lm <- rmse(test$price, pred_lm)
rmse_lm

## [1] 208007.3

Random Forest

rf_model <- randomForest(price ~ ., data = train, ntree = 100)
pred_rf <- predict(rf_model, test)
rmse_rf <- rmse(test$price, pred_rf)
rmse_rf

## [1] 137859.4

Model Comparison

results <- data.frame(Model = c("Linear Regression", "Random Forest"), RMSE = c(rmse_lm, rmse_rf))
results

##               Model     RMSE
## 1 Linear Regression 208007.3
## 2     Random Forest 137859.4

Conclusion

Random Forest provides a lower RMSE than Linear Regression, indicating better performance in predicting house prices.

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